Severe community-acquired pneumonia (CAP) remains a major cause of critical illness, yet there are no validated early clinical criteria to predict short-term treatment outcomes in these patients. Short-term pneumonia treatment outcomes are less affected by confounding factors introduced by a prolonged hospital course, and early prediction of short-term treatment outcomes can help physicians identify those who are likely to fail the current treatment and implement adjustments to existing diagnostic and therapeutic plans. Traditional clinical stability criteria such as Halm’s criteria are not calibrated for early outcome prediction in critically ill severe pneumonia patients. We applied the XGBoost algorithm to predict pneumonia cure by day 7-8 post- intubation with clinical features from days 1-3 in mechanically ventilated patients with severe CAP from the Successful Clinical Response in Pneumonia Therapy (SCRIPT) study, a prospective cohort study at a tertiary academic center. Pneumonia episodes were adjudicated for day 7-8 cure status by a panel of critical care physicians using a structured review process. Clinical features that inform Halm’s criteria, including vital signs, oxygenation parameters, mental status, and vasopressor use, were extracted from the electronic health record. We also examined model performance by including additional features, such as laboratory data, ventilator settings, and medications. Basic demographic characteristics including age and BMI were also incorporated. Among 85 patients, 42 (49.4%) were cured by day 7–8. The best-performing model, which used Halm’s clinical features and ventilator features from days 1–3, achieved a cross-validated AUROC of 0.757. Inclusion of lab and medication data did not significantly improve performance. Key predictors included GCS, norepinephrine requirement, and BMI. We prove the feasibility of using ML models to predict short-term treatment outcomes of severe CAP among critically ill patients with basic clinical features. Future studies should focus on external validation and clinical integration to inform prognosis and early reevaluation of treatment strategy in patients with predicted poor outcomes.

BDS holds US patent 10,905,706, “Compositions and methods to accelerate resolution of acute lung inflammation,” and serves on the scientific advisory board of Zoe Biosciences, in which he holds stock options. Other authors declare no conflicts of interest.

SCRIPT is supported by NIH/NIAID U19AI135964. Work in the Division of Pulmonary and Critical Care is also supported by Simpson Querrey Lung Institute for Translational Science (SQLIFTS) and the Canning Thoracic Institute. NSM is supported by AHA 24PRE1196998. GRSB is supported by the NIH (U19AI135964, P01AG049665, R01HL147575, P01HL071643, and R01HL154686); the US Department of Veterans Affairs (I01CX001777); a grant from the Chicago Biomedical Consortium; and a Northwestern University Dixon Translational Science Award. RGW is supported by NIH grants (U19AI135964, U01TR003528, P01HL154998, R01HL14988, and R01LM013337). AVM is supported by NIH grants (U19AI135964, P01AG049665, R21AG075423, R01HL158139, R01HL153312, and P01HL154998). BDS is supported by the NIH (R01HL149883, R01HL153122, P01HL154998, P01AG049665, and U19AI135964). AA is supported by NIH grants (U19AI135964 and R01HL158139). CAG is supported by NIH/NHLBI K23HL169815, a Parker B. Francis Opportunity Award, and an American Thoracic Society Unrestricted Grant.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

IRB of Northwestern University (STU00204868) gave ethical approval for this work

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes